1 citations · 1 across the 9 of their papers we have counts for
10 papers
: Decoding Discontinuous Cross-Modal Dynamics for Efficient Multimodal LLMs
Yingqi Fan, Anhao Zhao, Jinlan Fu +5
Multimodal Large Language Models (MLLMs) have achieved strong performance across vision-language tasks, but suffer from significant computational overhead due to the quadratic grow…
PricingLogic: Evaluating LLMs Reasoning on Complex Tourism Pricing Tasks
Yunuo Liu, Dawei Zhu, Zena Al-Khalili +5
We present PricingLogic, the first benchmark that probes whether Large Language Models(LLMs) can reliably automate tourism-related prices when multiple, overlapping fare rules appl…
Informed Routing in LLMs: Smarter Token-Level Computation for Faster Inference
Chao Han, Yijuan Liang, Zihao Xuan +3
The deployment of large language models (LLMs) in real-world applications is increasingly limited by their high inference cost. While recent advances in dynamic token-level computa…
LLaSO: A Foundational Framework for Reproducible Research in Large Language and Speech Model
Yirong Sun, Yizhong Geng, Peidong Wei +5
The development of Large Speech-Language Models (LSLMs) has been slowed by fragmented architectures and a lack of transparency, hindering the systematic comparison and reproducibil…
Context Guided Transformer Entropy Modeling for Video Compression
Junlong Tong, Wei Zhang, Yaohui Jin +1
Conditional entropy models effectively leverage spatio-temporal contexts to reduce video redundancy. However, incorporating temporal context often introduces additional model compl…
Reasoning Beyond Language: A Comprehensive Survey on Latent Chain-of-Thought Reasoning
Xinghao Chen, Anhao Zhao, Heming Xia +7
Large Language Models (LLMs) have shown impressive performance on complex tasks through Chain-of-Thought (CoT) reasoning. However, conventional CoT relies on explicitly verbalized…
MAER-Nav: Bidirectional Motion Learning Through Mirror-Augmented Experience Replay for Robot Navigation
Shanze Wang, Mingao Tan, Zhibo Yang +4
Deep Reinforcement Learning (DRL) based navigation methods have demonstrated promising results for mobile robots, but suffer from limited action flexibility in confined spaces. Con…
Enhancing Deep Reinforcement Learning-based Robot Navigation Generalization through Scenario Augmentation
Shanze Wang, Mingao Tan, Zhibo Yang +4
This work focuses on enhancing the generalization performance of deep reinforcement learning-based robot navigation in unseen environments. We present a novel data augmentation app…
Beyond Visibility Limits: A DRL-Based Navigation Strategy for Unexpected Obstacles
Mingao Tan, Shanze Wang, Biao Huang +4
Distance-based reward mechanisms in deep reinforcement learning (DRL) navigation systems suffer from critical safety limitations in dynamic environments, frequently resulting in co…
Dimension-variable Mapless Navigation with Deep Reinforcement Learning
Wei Zhang, Yunfeng Zhang, Ning Liu +1
Deep reinforcement learning (DRL) has exhibited considerable promise in the training of control agents for mapless robot navigation. However, DRL-trained agents are limited to the…